High-resolution side scan sonar systems, both real aperture and synthetic aperture, provide long range detection and classification of mines in the highly cluttered, shallow water, coastal environment (10 feet-80 feet water depths). However, interpreting sonar images can be quite difficult because much of the natural and man-made clutter is mine-like. This can cause sonar operator fatigue and result in many false calls and missed mines. These difficulties, as well as a recent trend towards unmanned minehunting systems, have highlighted the need for automated sonar image processing techniques to detect and classify mines.
Current image processing approaches for sonar images as well as other type of images focus on detecting an overall target signature. They employ a variety of techniques to reduce unwanted noise and to enhance the target, thereby increasing the signal-to-noise ratio (SNR) and the probability of detecting and classifying any target. In general, these approaches do increase the probability of detection and classification when all or most of the overall target signature is detectable in the image. However, because they are designed to detect the entire target signature, they fail to work well when only a portion of the actual target signature matches the desired target signature.
To understand this problem more fully, assume the goal is to detect a mine-like target that is cylindrical, e.g., six feet long and two feet in diameter. If the sonar system's pixel resolution is six inches by six inches, the ideal target signature for this type of target would be 12 pixels by 4 pixels. Hence, the overall target signature that for which the image would be scrutinized would be 12 pixels by 4 pixels ((6 feet)*(2 pixels/foot) by (2 feet)*(2 pixels/foot)). If only one-half of the mine-like target were visible, the actual target signature would never match the ideal overall target signature and the detection strategy would likely fail.